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- > For example, how nice would it be if every time tests fail, an LLM agent was kicked off with the task of figuring out why, and only notified us if it did before we fixed it?
You can use Git hooks to do that. If you have tests and one fails, spawn an instance of claude a prompt -p 'tests/test4.sh failed, look in src/ and try and work out why'
Or if, you use Gogs locally, you can add a Gogs hook to do the same on pre-push$ claude -p 'hello, just tell me a joke about databases' A SQL query walks into a bar, walks up to two tables and asks, "Can I JOIN you?" $> An example hook script to verify what is about to be pushed. Called by "git push" after it has checked the remote status, but before anything has been pushed. If this script exits with a non-zero status nothing will be pushed.
I like this idea. I think I shall get Claude to work out the mechanism itself :)
It is even a suggestion on this Claude cheet sheet
https://www.howtouselinux.com/post/the-complete-claude-code-...
by delaminator - >so I checked out the old version of the change with the bugs (yay Jujutsu!) and kicked off a fresh Claude Code session
There's a risk there that the AI could find the solution by looking through your history to find it, instead of discovering it directly in the checked-out code. AI has done that in the past:
by Thorrez - CLI terminals are incredibly powerful. They are also free if you use Gemini CLI or Qwen Code. Plus, you can access any OpenAI-compatible API(2k TPS via Cerebras at 2$/M or local models). And you can use them in IDEs like Zed with ACP mode.
All the simple stuff (creating a repo, pushing, frontend edits, testing, Docker images, deployment, etc.) is automated. For the difficult parts, you can just use free Grok to one-shot small code files. It works great if you force yourself to keep the amount of code minimal and modular. Also, they are great UIs—you can create smart programs just with CLI + MCP servers + MD files. Truly amazing tech.
by Frannky - This resonates with me a lot:
> As ever, I wish we had better tooling for using LLMs which didn’t look like chat or autocomplete
I think part of the reason why I was initially more skeptical than I ought to have been is because chat is such a garbage modality. LLMs started to "click" for me with Claude Code/Codex.
A "continuously running" mode that would ping me would be interesting to try.
by qsort - > Importantly, there is no need to trust the LLM or review its output when its job is just saving me an hour or two by telling me where the bug is, for me to reason about it and fix it.
Except they regularly come up with "explanations" that are completely bogus and may actually waste an hour or two. Don't get me wrong, LLMs can be incredibly helpful for identifying bugs, but you still have to keep a critical mindset.
by spacechild1 - > Full disclosure: Anthropic gave me a few months of Claude Max for free. They reached out one day and told me they were giving it away to some open source maintainers.
Related, lately I've been getting tons of Anthropic Instagram ads; they must be near a quarter of all the sponsored content I see for the last month or so. Various people vibe coding random apps and whatnot using different incarnations of Claude. Or just direct adverts to "Install Claude Code." I really have no idea why I've been targeted so hard, on Instagram of all places. Their marketing team must be working overtime.
by pton_xd - Personally my biggest piece of advice is: AI First.
If you really want to understand what the limitations are of the current frontier models (and also really learn how to use them), ask the AI first.
By throwing things over the wall to the AI first, you learn what it can do at the same time as you learn how to structure your requests. The newer models are quite capable and in my experience can largely be treated like a co-worker for "most" problems. That being said.. you also need to understand how they fail and build an intuition for why they fail.
Every time a new model generation comes up, I also recommend throwing away your process (outside of things like lint, etc.) and see how the model does without it. I work with people that have elaborate context setups they crafted for less capable models, they largely are un-neccessary with GPT-5-Codex and Sonnet 4.5.
by XenophileJKO - Using coding agents to track down the root cause of bugs like this works really well:
> Three out of three one-shot debugging hits with no help is extremely impressive. Importantly, there is no need to trust the LLM or review its output when its job is just saving me an hour or two by telling me where the bug is, for me to reason about it and fix it.
The approach described here could also be a good way for LLM-skeptics to start exploring how these tools can help them without feeling like they're cheating, ripping off the work of everyone who's code was used to train the model or taking away the most fun part of their job (writing code).
Have the coding agents do the work of digging around hunting down those frustratingly difficult bugs - don't have it write code on your behalf.
by simonw